A Sensitivity Approach to Assessing Model Uncertainty for Stochastic Systems

评估随机系统模型不确定性的灵敏度方法

• 批准号: 1400391
• 负责人: Henry Lam
• 金额: $ 22.49万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1400391&HistoricalAwards=false
• 关键词: Sensitivity; Approach; Assessing; Model; Uncertainty

The research objective of this award is to develop a robust and tractable methodology for assessing the impact of input model error, with minimal assumption placed on the parametric form of the input model. The discrepancy between model assumption and reality, or input model error, constitutes an important concern in many stochastic estimation problems, since it can affect the accuracy of outputs in various magnitudes. The key ingredient is a class of sensitivity estimators that are taken with respect to nonparametric statistical distances, derived via a new line of infinitesimal analysis on optimization problems over probability space. These sensitivity estimators are robust in that they automatically capture model discrepancy along the worst-case directions in the model space, and they are computationally tractable via efficient Monte Carlo methods. On the theoretical side, they will help provide fundamental understanding of model risk in stochastic computation, and on the practical side, they can be widely used to perform stress tests on the reliability of model assumptions.If successful, the results of this research will provide a robust and implementable method to assess the effect of model risk in computations that arise in many applications. Industries such as manufacturing, communication, call centers, and financial risk management all need to carry out calculations of different performance measures on a regular basis. The methodologies that come out from the research will provide sensitivity analysis tools to measure the impact and risk of inaccurate model assumptions in these calculations. The results of this research will also be used to develop new courses in undergraduate and graduate levels, and to establish mentorship of students from under-represented groups. The implementation will be widely disseminated through open software for both academic and industrial use.

该奖项的研究目标是开发一种可靠且可进行的方法来评估输入模型误差的影响，并对输入模型的参数形式放置了最小的假设。在许多随机估计问题中，模型假设与现实或输入模型误差之间的差异构成了一个重要问题，因为它可以影响各种幅度的输出的准确性。关键成分是一类灵敏度估计器，这些估计量相对于非参数统计距离进行了，该距离是通过针对概率空间优化问题的新的无限分析线得出的。这些灵敏度估计器具有鲁棒性，因为它们会自动沿模型空间中最差的方向捕获模型差异，并且通过有效的蒙特卡洛方法可以在计算上进行计算。从理论方面来说，它们将有助于对随机计算中的模型风险提供基本的了解，而在实际方面，它们可以广泛用于对模型假设的可靠性进行压力测试。如果成功的结果，这项研究的结果将提供强大且可实施的方法，以评估许多应用中出现的模型风险效果。制造，通信，呼叫中心和财务风险管理等行业都需要定期对不同的绩效指标进行计算。从研究中提出的方法将提供敏感性分析工具，以衡量这些计算中模型假设不准确的影响和风险。这项研究的结果还将用于开发本科和研究生级别的新课程，并建立来自代表性不足的群体的学生的指导。该实施将通过开放软件广泛传播，以供学术和工业使用。

项目成果

Uncertainty Quantification of Stochastic Simulation for Black-box Computer Experiments

黑盒计算机实验随机模拟的不确定性量化

• DOI: 10.1007/s11009-017-9599-7
• 发表时间: 2017
• 期刊: Methodology and Computing in Applied Probability
• 影响因子: 0.9
• 作者: Choe, Youngjun;Lam, Henry;Byon, Eunshin
• 通讯作者: Byon, Eunshin

Uncertainty quantification on simulation analysis driven by random forests

随机森林驱动的模拟分析的不确定性量化

• DOI: 10.1109/wsc.2017.8248044
• 发表时间: 2017
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Meisami, Amirhossein;Van Oyen, Mark P.;Lam, Henry
• 通讯作者: Lam, Henry

Computing worst-case expectations given marginals via simulation

通过模拟计算给定边际的最坏情况期望

• DOI: 10.1109/wsc.2017.8247962
• 发表时间: 2017
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Blanchet, Jose;He, Fei;Lam, Henry
• 通讯作者: Lam, Henry

Improving prediction from stochastic simulation via model discrepancy learning

通过模型差异学习改进随机模拟的预测

• DOI: 10.1109/wsc.2017.8247918
• 发表时间: 2017
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Lam, Henry;Zhang, Xinyu;Plumlee, Matthew
• 通讯作者: Plumlee, Matthew